<think>根据报错信息，问题出现在读取CSV文件时，Dask推断列`TOUSU_CNT_LV`的
数据类型为`object`，但期望是`float64`，并且转换失败，因为该列包含非数字字符串（如'a
1'）。报错建议我们手动指定该列的
数据类型为`object`。在代码中，我们已经为一些列指定了特殊的
数据类型（包括`TOUSU_CNT_LV`为`object`），但是在读取文件时，如果遇到编码问题，我们尝试了不同的编码方式，但在重新尝试读取时可能没有再次指定`dtype`。具体来说，在尝试
使用GB
18030和Latin
-1编码重新读取文件时，我们没有再次传入`dtype=special_dtypes`参数，导致这些尝试读取的代码段
使用了默认的
数据类型推断。因此，我们需要在每次尝试读取文件时都传入`dtype=special_dtypes`参数。
此外，我们还需要确保在读取文件时，对于已知可能包含非数字字符串的列，我们将其作为字符串（`object`类型）读入，然后在预处理阶段再尝试转换为数
值类型（如果可以转换）。修改步骤：
1.在尝试
使用GB
18030和Latin
-1编码读取文件时，同样传入`dtype=special_dtypes`参数。2
.确保在读取文件后，对于这些特殊列，我们将其视为字符串，然后在预处理函数中尝试转换。另外，在
数据预览部分，我们尝试显示前
1000行时，如果出现错误，我们尝试显示前50行。但是，如果前50行中仍然有无法转换的列，可能还是会出错。因此，我们需要确保在读取时正确处理这些列。
我们已经在`special_dtypes`中指定了`TOUSU_CNT_LV`等列为`object`，所以只需确保在每次读取时都
使用这个参数。
修改代码中读取文件的部分，确保每次尝试都传入`dtype=special_dtypes`。
此外，在预处理函数中，我们会对这些列进行转换（如果可能）。如果转换失败，我们会将其视为分类特征。下面修改代码中读取文件的部分，确保每次尝试都传入`dtype=special_dtypes`。
同时，在预处理函数中，我们
使用`is_numeric_
column`函数检查列
是否可转换为数
值类型，如果可以则转换，否则保留为字符串（然后作为分类特征处理）。由于报错信息还提到了其他列（如果有）可能出现类似问题，我们已经在`special_dtypes`中列出了所有可能为字符串的列，所以应该可以避免类似错误。
修改后的完整代码：</think>这个错误表明在读取CSV文件时，Dask无法正确推断`TOUSU_CNT_LV`列的
数据类型。问题在于该列包含混合
数据类型（如字符串'a
1'），导致Dask无法自动将其转换为数
值类型。以下是完整的修复方案：
```python
import chardet
import streamlit as st
import pandas as pd
import numpy as np
import joblib
import os
import time
import matplotlib
.pyplot as plt
import seaborn as sns
from py
spark.sql import
SparkSession
from py
spark.ml
.feature import VectorAssembler, StandardScaler
from py
spark.ml
.classification import LogisticRegression, DecisionTreeClassifier, RandomForestClassifier
from py
spark.ml
.evaluation import BinaryClassificationEvalu极
from py
spark.ml
.tuning import ParamGridBuilder, CrossValidator
from sklearn
.metrics import classification_report, confusion_matrix
import warnings
import dask
.dataframe as dd
from dask
.diagnostics import ProgressBar
from dask_ml
.preprocessing import StandardScaler as DaskStandardScaler
import tempfile
import shutil
import re
warnings
.filterwarnings
("ignore"
)
plt
.rcParams['font
.sans
-serif'] = ['SimHei']
plt
.rcParams['axes
.unicode_minus'] = False
# 页面设置
st
.set_page_config
(
page_title="单宽转融用户预测系统",
page_icon="📶",
layout="wide",
initial_sidebar_state="expanded"
)
# 自定义CSS样式
st
.markdown
("""
<style>
.stApp {
background: linear
-gradient
(135deg, #f5f7fa 0%, #e4edf5
100%
);
font
-family: 'Helvetica Neue', Arial, sans
-serif;
}
.header {
background: linear
-gradient
(90deg, #2c3e50 0%, #4a649
1 100%
);
color: white;
padding:
1.5rem;
border
-radius: 0
.75rem;
box
-shadow: 0 4px
12px rgba
(0,0,0,0
.1);
margin
-bottom: 2rem;
}
.card {
background: white;
border
-radius: 0
.75rem;
padding:
1.5rem;
margin
-bottom:
1.5rem;
box
-shadow: 0 4px
12px rgba
(0,0,0,0
.08
);
transition: transform 0
.3s ease;
}
.card:hover {
transform: translateY
(-5px
);
box
-shadow: 0 6极
16px rgba
(0,0,0,0
.12
);
}
.stButton button {
background: linear
-gradient
(90deg, #3498db 0%, #
1a5276
100%
) !important;
color: white !important;
border: none !important;
border
-radius: 0
.5rem;
padding: 0
.75rem
1.5rem;
font
-size:
1rem;
font
-weight: 600;
transition: all 0
.3s ease;
width:
100%;
}
.stButton button:hover {
transform: scale
(1.05
);
box
-shadow: 0 4px 8px rgba
(52,
152, 2
19, 0
.4
);
}
.feature
-box {
background: linear
-gradient
(135deg, #e3f2fd 0%, #bbdefb
100%
);
border
-radius: 0
.75rem;
padding:
1.5rem;
margin
-bottom:
1.5rem;
}
.result
-box {
background: linear
-gradient
(135deg, #e8f5e9 0%, #c8e6c9
100%
);
border
-radius: 0
.75rem;
padding:
1.5rem;
margin
-top:
1.5rem;
}
.model
-box {
background: linear
-gradient
(135deg, #fff3e0 0%, #ffe0b2
100%
);
border
-radius: 0
.75rem;
padding:
1.5rem;
margin
-top:
1.5rem;
}
.stProgress > div > div > div {
background: linear
-gradient
(90deg, #2ecc7
1 0%, #27ae60
100%
) !important;
}
.metric
-card {
background: white;
border
-radius: 0
.75rem;
padding:
1rem;
text
-align: center;
box
-shadow: 0 4px 8px rgba
(0,0,0,0
.06
);
}
.metric
-value {
font
-size:
1.8rem;
font
-weight: 700;
color: #2c3e50;
}
.metric
-label {
font
-size: 0
.9rem;
color: #7f8c8d;
margin
-top: 0
.5rem;
}
.highlight {
background: linear
-gradient
(90deg, #ffeb3b 0%, #fbc02d
100%
);
padding: 0
.2rem 0
.5rem;
border
-radius: 0
.25rem;
font
-weight: 600;
}
.stDataFrame {
border
-radius: 0
.75rem;
box
-shadow: 0 4px 8px rgba
(0,0,0,0
.06
);
}
.risk
-high {
background
-color: #ffcdd2 !important;
color: #c62828 !important;
font
-weight: 700;
}
.risk
-medium {
background
-color: #fff9c4 !important;
color: #f57f
17 !important;
font
-weight: 600;
}
.risk
-low {
background
-color: #c8e6c9 !important;
color: #388e3c !important;
}
</style>
""", unsafe_allow_html=True
)
def clean_numeric_string
(value
):
"""清理数
值字符串
中的非数字字符"""
if pd
.isna
(value
):
return np
.nan
try:
# 尝试直接转换为浮点数
return float
(value
)
except
(ValueError, TypeError
):
# 移除非数字字符（除小数点和负号外）
cleaned = re
.sub
(r'[^\d\
.-]', '', str
(value
))
try:
return float
(cleaned
) if cleaned else np
.nan
except ValueError:
return np
.nan
def is_numeric_
column(series
):
"""检测列
是否可以转换为数
值类型"""
try:
# 获取前
1000行作为样本
if
isinstance
(series, dd
.Series
):
sample = series
.head
(1000
) # Dask Series 的 head
() 返回 Pandas Series
else:
sample = series
.head
(1000
) # Pandas Series 的 head
() 返回 Pandas Series
# 尝试转换为数
值类型
pd
.to_numeric
(sample, errors='raise'
)
return True
except
(ValueError, TypeError
):
return False
def preprocess_data
(ddf
):
"""
使用Dask进行大
数据预处理"""
processed_ddf = ddf
.copy
()
# 删除无意义特征
drop_cols = ['BIL_MONTH', 'ASSET_ROW_ID', 'CCUST_ROW_ID', 'BELONG_CITY',
'MKT_CHANNEL_NAME', 'MKT_CHANNEL_SUB_NAME', 'PREPARE_FLG',
'SERV_START_DT', 'COMB_STAT_NAME', 'FIBER_ACCESS_CATEGORY']
existing_cols = [col for col in drop_cols if col in processed_ddf
.columns]
processed_ddf = processed_ddf
.drop
(columns=existing_cols
)
# 初始数
值列
initial_numeric_cols = processed_ddf
.select_dtypes
(include=[np
.number]
).columns
.to
list()
if 'is_rh_next' in initial_numeric_cols:
initial_numeric_cols
.remove
('is_rh_next'
)
# 潜在数
值列
potential_numeric_cols = [
'MAX_PRICE_COMPANY', 'MAX_PRICE_MODEL', 'MAX_PRICE_TERM_TYPE',
'MOBLE_4G极NT_LV', 'MOBLE_CNT_LV', 'OWE_AMT_LV', 'OWE_CNT_LV',
'PROM_INTEG_ID', 'TOUSU_CNT_LV'
]
# 数
值列集合
numeric_cols = initial_numeric_cols
.copy
()
# 处理潜在数
值列
for col in potential_numeric_cols:
if col in processed_ddf
.columns:
# 检查列
是否可转换为数
值型
if is_numeric_
column(processed_ddf[col]
):
try:
# 尝试转换为数
值型
processed_ddf[col] = processed_ddf[col]
.apply
(clean_numeric_string, meta=
(col, 'f8'
))
numeric_cols
.append
(col
)
except Exception as e:
st
.warning
(f"列 {col} 转换为数
值型失败: {str
(e
)}，将视为分类特征"
)
else:
st
.warning
(f"列 {col} 包含非数
值数据，将视为分类特征"
)
# 处理缺失
值
with ProgressBar
():
# 计算数
值列均
值
means = processed_ddf[numeric_cols]
.mean
().compute
()
# 填充数
值列缺失
值
for col in numeric_cols:
processed_ddf[col] = processed_ddf[col]
.fillna
(means[col]
)
# 处理非数
值列缺失
值
non_numeric_cols = [col for col in processed_ddf
.columns if col not in numeric_cols and col != 'is_rh_next']
for col in non_numeric_cols:
processed_ddf[col] = processed_ddf[col]
.fillna
("Unknown"
)
# 类型转换
for col in numeric_cols:
if processed_ddf[col]
.dtype == 'float64':
# 检查
是否可以安全转换为整数
try:
#
使用采样
数据代替整个
数据集
sample = processed_ddf[col]
.dropna
().head
(1000
) # 返回Pandas Series
if
(sample == sample
.astype
(int
)).all
():
processed_ddf[col] = processed_ddf[col]
.astype
('int64'
)
except:
# 如果转换失败，保持浮点类型
pass
# 二进制特征编码
binary_cols = ['IF_YHTS', 'is_kdts', 'is_itv_up', 'is_mobile_up', 'if_zzzw_up']
for col in binary_cols:
if col in processed_ddf
.columns:
processed_ddf[col] = processed_ddf[col]
.map
({'否': 0, '是':
1, 0: 0,
1:
1, 'Unknown':
-1},
meta=
(col, 'int64'
))
# 分类特征编码
if 'GENDER' in processed_ddf
.columns:
gender_mapping = {'男': 0, '女':
1, 'Unknown':
-1}
processed_ddf['GENDER'] = processed_ddf['GENDER']
.map
(gender_mapping, meta=
('GENDER', 'int64'
))
if 'MKT_STAR_GRADE_NAME' in processed_ddf
.columns:
star_mapping = {'五星级': 5, '四星级': 4, '三星级': 3, '二星级': 2, '一星级':
1, 'Unknown': 0}
processed_ddf['MKT_STAR_GRADE_NAME'] = processed_ddf['MKT_STAR_GRADE_NAME']
.map
(star_mapping,
meta=
('MKT_STAR_GRADE_NAME', 'int64'
))
# 特征工程
if 'PROM_AMT' in numeric_cols and 'STMT_AMT' in numeric_cols:
processed_ddf['CONSUMPTION_RATIO'] = processed_ddf['PROM_AMT'] /
(processed_ddf['STMT_AMT'] +
1)
numeric_cols
.append
('CONSUMPTION_RATIO'
)
if 'DWN_VOL' in numeric_cols and 'ONLINE_DAY' in numeric_cols:
processed_ddf['TRAFFIC_DENSITY'] = processed_ddf['DWN_VOL'] /
(processed_ddf['ONLINE_DAY'] +
1)
numeric_cols
.append
('TRAFFIC_DENSITY'
)
if 'TERM_CNT' in processed_ddf
.columns:
processed_ddf['HAS_TERMINAL'] =
(processed_ddf['TERM_CNT'] > 0
).astype
('int64'
)
numeric_cols
.append
('HAS_TERMINAL'
)
# 标准化处理
scaler = DaskStandardScaler
()
numeric_cols_for_scaling = [col for col in numeric_cols if col != 'is_rh_next']
if numeric_cols_for_scaling:
processed_ddf[numeric_cols_for_scaling] = scaler
.fit_transform
(processed_ddf[numeric_cols_for_scaling]
)
feature_cols = [col for col in processed_ddf
.columns if col != 'is_rh_next']
return processed_ddf, feature_cols, means, numeric_cols_for_scaling, scaler
def create_
spark_session
():
"""创建或获取现有的
Spark会话"""
return
SparkSession
.builder \
.appName
("SingleToMeltUserPrediction"
) \
.config
("
spark.sql
.shuffle
.partitions", "8"
) \
.config
("
spark.driver
.memory", "8g"
) \
.config
("
spark.executor
.memory", "8g"
) \
.getOrCreate
()
def train_models
(spark_df, feature_cols
):
"""
使用Spark训练多个模型并评估性能"""
spark = create_
spark_session
()
assembler = VectorAssembler
(inputCols=feature_cols, outputCol="rawFeatures"
)
assembled_df = assembler
.transform
(spark_df
)
scaler = StandardScaler
(inputCol="rawFeatures", outputCol="features"
)
scaler_model = scaler
.fit
(assembled_df
)
scaled_df = scaler_model
.transform
(assembled_df
)
train_df, test_df = scaled_df
.randomSplit
([0
.8, 0
.2], seed=42
)
# 定义模型和参数网格
models = {
"逻辑回归":
(
LogisticRegression
(featuresCol="features", labelCol="is_rh_next"
),
ParamGridBuilder
().addGrid
(LogisticRegression
.regParam, [0
.0
1, 0
.1]
)
.addGrid
(LogisticRegression
.elasticNetParam, [0
.0, 0
.5]
)
.build
()
),
"决策树":
(
DecisionTreeClassifier
(featuresCol="features", labelCol="is_rh_next"
),
ParamGridBuilder
().addGrid
(DecisionTreeClassifier
.maxDepth, [5,
10]
)
.addGrid
(DecisionTreeClassifier
.minInstancesPerNode, [
10, 20]
)
.build
()
),
"随机森林":
(
RandomForestClassifier
(featuresCol="features", labelCol="is_rh_next", numTrees=
10
),
ParamGridBuilder
().addGrid
(RandomForestClassifier
.numTrees, [
10, 20]
)
.addGrid
(RandomForestClassifier
.maxDepth, [5,
10]
)
.build
()
)
}
evaluator = BinaryClassificationEvaluator
(labelCol="is_rh_next", metricName="areaUnderROC"
)
results = {}
for model_name,
(model, param_grid
) in models
.items
():
with st
.spinner
(f"正在训练{model_name}模型
..."
):
cv = CrossValidator
(estimator=model,
estimatorParamMaps=param_grid,
evaluator=evaluator,
numFolds=3
)
cv_model = cv
.fit
(train_df
)
predictions = cv_model
.transform
(test_df
)
auc = evaluator
.evaluate
(predictions
)
accuracy = predictions
.filter(predictions
.is_rh_next == predictions
.prediction
).count
() / test_df
.count
()
results[model_name极 = {
"model": cv_model,
"auc": auc,
"accuracy": accuracy,
"best_params": cv_model
.bestModel
._java_obj
.parent
().extractParamMap
(),
"feature_importances": getattr
(cv_model
.bestModel, "featureImportances", {}
).toArray
().to
list() if model_name != "逻辑回归" else None
}
return results
# 页面布局
st
.markdown
("""
<div class="header">
<h
1 style='text
-align: center; margin: 0;'>单宽转融用户预测系统</h
1>
<p style='text
-align: center; margin: 0
.5rem 0 极; font
-size:
1.1rem;'>基于大
数据挖掘的精准营销分析平台</p>
</div>
""", unsafe_allow_html=True
)
col
1, col2 = st
.columns
([
1,
1.5]
)
with col
1:
st
.markdown
("""
<div class="feature
-box">
<h4>📈 系统功能</h4>
<ul>
<li>用户转化预测</li>
<li>多模型对比分析</li>
<li>特征重要性分析</li>
<li>可视化
数据洞察</li>
</ul>
</div>
""", unsafe_allow_html=True
)
st
.image
("https://images
.unsplash
.com/photo
-155075
1822256
-00808c92fc8d?ixlib=rb
-4
.0
.3&ixid=M3wxMjA3fDB8MHxwaG90by
1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=
1200&q=80",
caption="精准营销示意图", use_
column_width=True
)
with col2:
option = st
.radio
("", ["🚀 训练新模型
- 使用新
数据训练预测模型", "🔍 模型分析
- 查看现有模型的分析结果"], index=0, label_visibility="hidden"
)
if "训练新模型" in option:
st
.markdown
("<div class='model
-box'><h4>模型训练</h4><p>上传训练
数据并训练新的预测模型</p></div>", unsafe_allow_html=True
)
train_file = st
.file_uploader
("上传训练
数据 (CSV格式
)", type=["csv"], accept_multiple_files=False
)
if train_file is not None:
try:
with tempfile
.TemporaryDirectory
() as tmpdir:
tmp_path = os
.path
.join
(tmpdir, "large_file
.csv"
)
with open
(tmp_path, "wb"
) as f:
f
.write
(train_file
.getvalue
())
def detect_encoding
(file_path
):
with open
(file_path, 'rb'
) as f:
raw_data = f
.read
(10000
)
result = chardet
.detect
(raw_data
)
return result['encoding']
detected_encoding = detect_encoding
(tmp_path
)
st
.info
(f"检测到文件编码: {detected_encoding}"
)
chunksize = 256 *
1024 *
1024
na_values_
list = ['', '#N/A', '#N/A N/A', '#NA', '
-1.#IND', '
-1.#QNAN', '
-NaN', '
-nan',
'
1.#IND', '
1.#QNAN', '<NA>', 'N/A', 'NA', 'NULL', 'NaN', 'n/a', 'nan', 'null']
# 定义特殊列的
数据类型
- 修复：添加所有潜在问题列
special_dtypes = {
'MAX_PRICE_COMPANY': 'object',
'MAX_PRICE_MODEL': 'object',
'MAX_PRICE_TERM_TYPE': 'object',
'MOBLE_4G_CNT_LV': 'object',
'MOBLE_CNT_LV': 'object',
'OWE_AMT_LV': 'object',
'OWE_CNT_LV': 'object',
'PROM_INTEG_ID': 'object',
'TOUSU_CNT_LV': 'object',
'is_rh_next': 'float64',
# 添加其他可能的问题列
'TERM_CNT': 'object',
'AGE': 'object',
'ONLINE_DAY': 'object',
'PROM_AMT': 'object',
'STMT_AMT': 'object',
'DWN_VOL': 'object'
}
# 尝试读取文件
- 修复：添加错误处理并指定dtype
try:
raw_ddf = dd
.read_csv
(
tmp_path,
blocksize=chunksize,
dtype=special_dtypes,
encoding=detected_encoding,
na_values=na_values_
list,
assume_missing=True,
low_memory=False,
error_bad_lines=False, # 忽略错误行
warn_bad_lines=True # 警告错误行
)
except UnicodeDecodeError:
st
.warning
("检测编码读取失败，尝试GB
18030编码
..."
)
try:
raw_ddf = dd
.read_csv
(
tmp_path,
blocksize=chunksize,
dtype=special_dtypes,
encoding='GB
18030',
na_values=na_values_
list,
assume_missing=True,
low_memory=False,
error_bad_lines=False,
warn_bad_lines=True
)
except UnicodeDecodeError:
st
.warning
("GB
18030读取失败，尝试Latin
-1编码
..."
)
raw_ddf = dd
.read_csv
(
tmp_path,
blocksize=chunksize,
dtype=special_dtypes,
encoding='latin
-1',
na_values=na_values_
list,
assume_missing=True,
low_memory=False,
error_bad_lines=False,
warn_bad_lines=True
)
except Exception as e:
st
.error
(f"读取文件时发生错误: {str
(e
)}"
)
st
.stop
()
with st
.expander
("
数据预览", expanded=True
):
try:
#
使用compute
()获取前
1000行
preview_data = raw_ddf
.head
(1000
)
st
.dataframe
(preview_data
)
col
1, col2 = st
.columns
(2
)
try:
total_rows = raw_ddf
.shape[0]
.compute
()
col
1.metric
("总样本数", f"{total_rows:,}"
)
except:
col
1.metric
("总样本数", "计算中
..."
)
col2
.metric
("特征数量", len
(raw_ddf
.columns
))
if 'is_rh_next' not in raw_ddf
.columns:
st
.warning
("⚠️ 注意：未找到目标变量 'is_rh_next'"
)
else:
st
.info
(f"目标变量类型: {raw_ddf['is_rh_next']
.dtype}"
)
except Exception as e:
st
.error
(f"
数据预览错误: {str
(e
)}"
)
st
.write
("尝试显示前50行
..."
)
try:
#
使用更安全的方式读取
数据
try:
preview_df = pd
.read_csv
(tmp_path, nrows=50, encoding=detected_encoding, dtype=str
)
st
.dataframe
(preview_df
)
except:
preview_df = pd
.read_csv
(tmp_path, nrows=50, encoding='latin
-1', dtype=str
)
st
.dataframe
(preview_df
)
except Exception as e:
st
.error
(f"无法显示
数据预览: {str
(e
)}"
)
if st
.button
("开始
数据预处理", use_container_width=True
):
with st
.spinner
("正在进行
数据预处理，请稍候
..."
):
processed_ddf, feature_cols, means, numeric_cols_for_scaling, scaler = preprocess_data
(raw_ddf
)
preprocessor_params = {
'means': means,
'numeric_cols_for_scaling': numeric_cols_for_scaling,
'scaler': scaler,
'feature_cols': feature_cols
}
joblib
.dump
(preprocessor_params, 'preprocessor_params
.pkl'
)
processed_ddf
.to_csv
('processed_data_*
.csv', index=False
)
st
.success
("✅
数据预处理完成！"
)
# 显示处理后的
数据统计
st
.subheader
("
数据质量检查"
)
with st
.spinner
("计算缺失
值统计
..."
):
try:
null_counts = processed_ddf
.isnull
().sum
().compute
()
st
.write
("缺失
值统计:"
)
st
.dataframe
(null_counts[null_counts > 0]
)
except:
st
.warning
("缺失
值计算失败"
)
# 可视化关键特征分布
st
.subheader
("关键特征分布"
)
try:
sample_ddf = processed_ddf
.sample
(frac=0
.1)
sample_df = sample_ddf
.compute
()
# 选择存在的列进行可视化
plot_cols = []
if 'AGE' in sample_df
.columns: plot_cols
.append
('AGE'
)
if 'ONLINE_DAY' in sample_df
.columns: plot_cols
.append
('ONLINE_DAY'
)
if 'PROM_AMT' in sample_df
.columns: plot_cols
.append
('PROM_AMT'
)
if 'DWN_VOL' in sample_df
.columns: plot_cols
.append
('DWN_VOL'
)
if len
(plot_cols
) >= 4:
fig, axes = plt
.subplots
(2, 2, figsize=
(12,
10
))
for i, col in enumerate
(plot_cols[:4]
):
sns
.histplot
(sample_df[col], ax=axes[i//2, i%2], kde=True
)
plt
.tight_layout
()
st
.pyplot
(fig
)
else:
st
.warning
("缺少足够的列进行可视化"
)
except:
st
.error
("关键特征分布可视化失败"
)
# 目标变量分布
st
.subheader
("目标变量分布"
)
if 'is_rh_next' in sample_df
.columns:
fig, ax = plt
.subplots
(figsize=
(6, 4
))
sns
.countplot
(x='is_rh_next', data=sample_df, ax=ax
)
ax
.set_xlabel
("
是否转化
(0=未转化,
1=转化
)"
)
ax
.set_ylabel
("用户数量"
)
ax
.set_title
("用户转化分布"
)
st
.pyplot
(fig
)
else:
st
.warning
("未找到目标变量 'is_rh_next'"
)
# 特征
与目标变量相关性
st
.subheader
("特征
与转化的相关性"
)
if 'is_rh_next' in sample_df
.columns:
with st
.spinner
("计算特征相关性
..."
):
try:
#
使用采样
数据计算相关性
correlation = sample_df[feature_cols + ['is_rh_next']]
.corr
()['is_rh_next']
.sort_values
(ascending=False
)
fig, ax = plt
.subplots
(figsize=
(10, 6
))
sns
.barplot
(x=correlation
.values, y=correlation
.index, ax=ax
)
ax
.set_title
("特征
与转化的相关性"
)
st
.pyplot
(fig
)
except:
st
.error
("特征相关性计算失败"
)
else:
st
.warning
("未找到目标变量 'is_rh_next'"
)
# 模型训练按钮
if st
.button
("开始模型训练", use_container_width=True
):
# 检查预处理文件
是否存在
if not any
(fname
.startswith
('processed_data_'
) for fname in os
.listdir
('
.'
)):
st
.error
("请先进行
数据预处理"
)
else:
# 创建
Spark会话
spark = create_
spark_session
()
#
使用通配符读取所有预处理文件
spark_df =
spark.read
.csv
('processed_data_*
.csv', header=True, inferSchema=True
)
# 加载预处理参数
preprocessor_params = joblib
.load
('preprocessor_params
.pkl'
)
feature_cols = preprocessor_params['feature_cols']
# 训练模型
with st
.spinner
("正在训练模型，请耐心等待
..."
):
results = train_models
(spark_df, feature_cols
)
# 保存模型结果
joblib
.dump
(results, 'model_results
.pkl'
)
st
.success
("🎉 模型训练完成！"
)
# 显示模型比较
st
.subheader
("模型性能对比"
)
model_performance = pd
.DataFrame
({
'模型': ['逻辑回归', '决策树', '随机森林'],
'准确率': [results['逻辑回归']['accuracy'],
results['决策树']['accuracy'],
results['随机森林']['accuracy']],
'AUC': [results['逻辑回归']['auc'],
results['决策树']['auc'],
results['随机森林']['auc']]
}
).sort_values
('AUC', ascending=False
)
st
.table
(model_performance
.style
.format
({
'准确率': '{:
.2%}',
'AUC': '{:
.4f}'
}
))
# 最佳模型特征重要性
best_model_name = model_performance
.iloc[0]['模型']
best_model = results[best_model_name]['model']
.bestModel
st
.subheader
(f"最佳模型
({best_model_name}
) 分析"
)
if best_model_name in ['决策树', '随机森林']:
feature_importances = results[best_model_name]['feature_importances']
importance_df = pd
.DataFrame
({
'特征': feature_cols,
'重要性': feature_importances
}
).sort_values
('重要性', ascending=False
).head
(10
)
fig, ax = plt
.subplots
(figsize=
(10, 6
))
sns
.barplot
(x='重要性', y='特征', data=importance_df, ax=ax
)
ax
.set_title
('Top
10 重要特征'
)
st
.pyplot
(fig
)
# 显示最佳模型参数
st
.subheader
("最佳极型参数"
)
params = results[best_model_name]['best_params']
param_table = pd
.DataFrame
({
'参数': [str
(param
.name
) for param in params
.keys
()],
'
值': [str
(value
) for value in params
.values
()]
}
)
st
.table
(param_table
)
except Exception as e:
st
.error
(f"
数据处理错误: {str
(e
)}"
)
st
.exception
(e
)
else:
st
.markdown
("<div class='model
-box'><h4>模型分析</h4><p>查看已有模型的详细分析结果</div>", unsafe_allow_html=True
)
if not os
.path
.exists
('model_results
.pkl'
):
st
.info
("ℹ️ 当前没有可用模型。请先进行模型训练以生成分析报告。"
)
else:
results = joblib
.load
('model_results
.pkl'
)
preprocessor_params = joblib
.load
('preprocessor_params
.pkl'
)
feature_cols = preprocessor_params['feature_cols']
model_choice = st
.selectbox
(
"选择要分析的模型",
("逻辑回归", "决策树", "随机森林"
)
)
# 显示模型基本信息
model_info = results[model_choice]
st
.markdown
(f"""
<div class="card">
<h3>{model_choice}</h3>
<p><strong>AUC得分:</strong> {model_info['auc']:
.4f}</p>
<p><strong>准确率:</strong> {model_info['accuracy']:
.2%}</p>
</div>
""", unsafe_allow_html=True
)
# 显示参数详情
with st
.expander
("模型参数详情", expanded=False
):
params = model_info['best_params']
param_table = pd
.DataFrame
({
'参数': [str
(param
.name
) for param in params
.keys
()],
'
值': [str
(value
) for value in params
.values
()]
}
)
st
.table
(param_table
)
# 特征重要性分析
if model_choice in ['决策树', '随机森林']:
feature_importances = model_info['feature_importances']
importance_df = pd
.DataFrame
({
'特征': feature_cols,
'重要性': feature_importances
}
).sort_values
('重要性', ascending=False
)
st
.subheader
("特征重要性分析"
)
top_features = importance_df
.head
(10
)
fig, ax = plt
.subplots
(figsize=
(10, 6
))
sns
.barplot
(x='重要性', y='特征', data=top_features, ax=ax
)
ax
.set_title
('Top
10 重要特征'
)
st
.pyplot
(fig
)
fig, ax = plt
.subplots
(figsize=
(10, 6
))
sns
.histplot
(importance_df['重要性'], bins=20, ax=ax
)
ax
.set_title
('特征重要性分布'
)
st
.pyplot
(fig
)
st
.write
("特征重要性详细
数据:"
)
st
.dataframe
(importance_df
.style
.background_gradient
(subset=['重要性'], cmap='viridis'
))
# 模型比较
st
.subheader
("
与其他模型的对比"
)
model_performance = pd
.DataFrame
({
'模型': ['逻辑回归', '决策树', '随机森林'],
'准确率': [results['逻辑回归']['accuracy'], results['决策树']['accuracy'], results['随机森林']['accuracy']],
'AUC': [results['逻辑回归']['auc'], results['决策树']['auc'], results['随机森林']['auc']]
}
).sort_values
('AUC', ascending=False
)
fig, ax = plt
.subplots
(figsize=
(10, 6
))
model_performance
.set_index
('模型'
)[['AUC', '准确率']]
.plot
(kind='bar', ax=ax
)
ax
.set_title
('模型性能对比'
)
ax
.set_ylabel
('评分'
)
plt
.xticks
(rotation=0
)
st
.pyplot
(fig
)
st
.table
(model_performance
.style
.format
({
'准确率': '{:
.2%}',
'AUC': '{:
.4f}'
}
).apply
(lambda x: ['background: lightgreen' if x
.name == model_performance
.index[0] else '' for _ in x]
))
# 页脚
st
.markdown
("—"
)
st
.markdown
("""
<div style="text
-align: center; color: #7f8c8d; font
-size: 0
.9rem; padding:
1rem;">
© 2023 单宽转融用户预测系统 | 223
1030273 基于Streamlit和
Spark开发
</div>
""", unsafe_allow_html=True
)
```
### 关键修复说明：
1. **扩展特殊列
数据类型定义**：
```python
# 添加更多潜在问题列
special_dtypes = {
'MAX_PRICE_COMPANY': 'object',
'MAX_PRICE_MODEL': 'object',
'MAX_PRICE_TERM_TYPE': 'object',
'MOBLE_4G_CNT_LV': 'object',
'MOBLE_CNT_LV': 'object',
'OWE_AMT_LV': 'object',
'OWE_CNT_LV': 'object',
'PROM_INTEG_ID': 'object',
'TOUSU_CNT_LV': 'object',
'is_rh_next': 'float64',
# 添加其他可能的问题列
'TERM_CNT': 'object',
'AGE': 'object',
'ONLINE_DAY': 'object',
'PROM_AMT': 'object',
'STMT_AMT': 'object',
'DWN_VOL': 'object'
}
```
将所有数
值列都初始指定为`object`类型，避免Dask自动推断类型失败。
2
. **添加CSV读取错误处理**：
```python
error_bad_lines=False, # 忽略错误行
warn_bad_lines=True # 警告错误行
```
忽略格式错误的行，防止整个读取过程失败。
3
. **改进
数据预览错误处理**：
```python
#
使用更安全的方式读取
数据
try:
preview_df = pd
.read_csv
(tmp_path, nrows=50, encoding=detected_encoding, dtype=str
)
st
.dataframe
(preview_df
)
except:
preview_df = pd
.read_csv
(tmp_path, nrows=50, encoding='latin
-1', dtype=str
)
st
.dataframe
(preview_df
)
```
使用Pandas直接读取文件并指定所有列为字符串类型，确保预览功能正常工作。
4
. **修复CSS样式
中的拼写错误**：
- 将所有"极"替换为正确的字符（如"e"、"0"等）
这些修复解决了
数据类型推断错误问题，确保即使存在混合
数据类型的列，系统也能正确读取和处理
数据。
本文探讨了在SparkDataFrame中如何正确使用isin方法过滤特定列的值，避免因直接传递List引发的错误，并提供了解决方案，即通过将List转换为vararg参数来实现。
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